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The biosphere genomics era is transforming life science research, but existing methods struggle to efficiently reduce the vast dimensionality of the protein universe. We present DIAMOND DeepClust, an ultra-fast cascaded clustering method optimized to cluster the 19 billion protein sequences currently defining the protein biosphere. As a result, we detect 1.7 billion clusters of which 32% hold more than one sequence. This means that 544 million clusters represent 94% of all known proteins, illustrating that clustering across the tree of life can significantly accelerate comparative studies in the Earth BioGenome era.

All multicellular organisms are closely associated with microbes, which have a major impact on the health of their host. The interactions of microbes among themselves and with the host take place at the microscale, forming complex networks and spatial patterns that are rarely well understood due to the lack of suitable analytical methods. The importance of high-resolution spatial molecular information has become widely appreciated with the recent advent of spatially resolved transcriptomics. Here, we present Spatial metaTranscriptomics (SmT), a sequencing-based approach that leverages 16S/18S/ITS/poly-d(T) multimodal arrays for simultaneous host transcriptome- and microbiome-wide characterization of tissues at 55-µm resolution. We showcase SmT in outdoor-grown Arabidopsis thaliana leaves as a model system, and found tissue-scale bacterial and fungal hotspots. By network analysis, we study inter- and intra-kingdom spatial interactions among microbes, as well as the host response to microbial hotspots. SmT is a powerful new strategy that will be pivotal to answering fundamental questions on host-microbiome interplay.

The community structure in the plant-associated microbiome depends collectively on host–microbe, microbe–microbe and host–microbe–microbe interactions. The ensemble of interactions between the host and microbial consortia may lead to outcomes that are not easily predicted from pairwise interactions. Plant–microbe–microbe interactions are important to plant health but could depend on both host and microbe strain variation. Here we study interactions between groups of naturally co-existing commensal and pathogenic Pseudomonas strains in the Arabidopsis thaliana phyllosphere. We find that commensal Pseudomonas prompt a host response that leads to selective inhibition of a specific pathogenic lineage, resulting in plant protection. The extent of protection depends on plant genotype, supporting that these effects are host-mediated. Strain-specific effects are also demonstrated by one individual Pseudomonas isolate eluding the plant protection provided by commensals. Our work highlights how within-species genetic differences in both hosts and microbes can affect host–microbe–microbe dynamics., Nature Ecology & Evolution, 6 (4), ISSN:2397-334X

Hybrid necrosis in plants arises from conflict between divergent alleles of immunity genes contributed by different parents, resulting in autoimmunity. We investigate a severe hybrid necrosis case in Arabidopsis thaliana, where the hybrid does not develop past the cotyledon stage and dies 3 weeks after sowing. Massive transcriptional changes take place in the hybrid, including the upregulation of most NLR (nucleotide-binding site leucine-rich repeat) disease-resistance genes. This is due to an incompatible interaction between the singleton TIR-NLR gene DANGEROUS MIX 10 (DM10), which was recently relocated from a larger NLR cluster, and an unlinked locus, DANGEROUS MIX 11 (DM11). There are multiple DM10 allelic variants in the global A. thaliana population, several of which have premature stop codons. One of these, which has a truncated LRR–PL (leucine-rich repeat [LRR]–post-LRR) region, corresponds to the DM10 risk allele. The DM10 locus and the adjacent genomic region in the risk allele carriers are highly differentiated from those in the nonrisk carriers in the global A. thaliana population, suggesting that this allele became geographically widespread only relatively recently. The DM11 risk allele is much rarer and found only in two accessions from southwestern Spain—a region from which the DM10 risk haplotype is absent—indicating that the ranges of DM10 and DM11 risk alleles may be nonoverlapping.

Patterns observed by examining the evolutionary relationships among proteins of common origin can reveal the structural and functional importance of specific residue positions. In particular, amino acids that are highly conserved (i.e., their positions evolve at a slower rate than other positions) are particularly likely to be of biological importance, for example, for ligand binding. ConSurf is a bioinformatics tool for accurately estimating the evolutionary rate of each position in a protein family. Here we introduce a new release of ConSurf‐DB, a database of precalculated ConSurf evolutionary conservation profiles for proteins of known structure. ConSurf‐DB provides high‐accuracy estimates of the evolutionary rates of the amino acids in each protein. A reliable estimate of a query protein's evolutionary rates depends on having a sufficiently large number of effective homologues (i.e., nonredundant yet sufficiently similar). With current sequence data, ConSurf‐DB covers 82% of the PDB proteins. It will be updated on a regular basis to ensure that coverage remains high—and that it might even increase. Much effort was dedicated to improving the user experience. The repository is available at https://consurfdb.tau.ac.il/. Broader audience By comparing a protein to other proteins of similar origin, it is possible to determine the extent to which each amino acid position in the protein evolved slowly or rapidly. A protein's evolutionary profile can provide valuable insights: For example, amino acid positions that are highly conserved (i.e., evolved slowly) are particularly likely to be of structural and/or functional importance, for example, for ligand binding and catalysis. We introduce here a new and improved version of ConSurf‐DB, a continually updated database that provides precalculated evolutionary profiles of proteins with known structure.

Plants are protected from pathogens not only by their own immunity but often also by colonizing commensal microbes. In Arabidopsis thaliana, a group of cryptically pathogenic Pseudomonas strains often dominates local populations. This group coexists in nature with commensal Pseudomonas strains that can blunt the deleterious effects of the pathogens in the laboratory. We have investigated the interaction between one of the Pseudomonas pathogens and 99 naturally co-occurring commensals, finding plant protection to be common among non-pathogenic Pseudomonas. While protective ability is enriched in one specific lineage, there is also a substantial variation for this trait among isolates of this lineage. These functional differences do not align with core-genome phylogenies, suggesting repeated gene inactivation or loss as causal. Using genome-wide association, we discovered that different bacterial genes are linked to plant protection in each lineage. We validated a protective role of several lineage-specific genes by gene inactivation, highlighting iron acquisition and biofilm formation as prominent mechanisms of plant protection in this Pseudomonas lineage. Collectively, our work illustrates the importance of functional redundancy in plant protective traits across an important group of commensal bacteria.

Plants produce diverse metabolites to cope with the challenges presented by complex and ever-changing environments. These challenges drive the diversification of specialized metabolites within and between plant species. However, we are just beginning to understand how frequently new alleles arise controlling specialized metabolite diversity and how the geographic distribution of these alleles may be structured by ecological and demographic pressures. Here, we measure the variation in specialized metabolites across a population of 797 natural Arabidopsis thaliana accessions. We show that a combination of geography, environmental parameters, demography and different genetic processes all combine to influence the specific chemotypes and their distribution. This showed that causal loci in specialized metabolism contain frequent independently generated alleles with patterns suggesting potential within-species convergence. This provides a new perspective about the complexity of the selective forces and mechanisms that shape the generation and distribution of allelic variation that may influence local adaptation., eLife digest Since plants cannot move, they have evolved chemical defenses to help them respond to changes in their surroundings. For example, where animals run from predators, plants may produce toxins to put predators off. This approach is why plants are such a rich source of drugs, poisons, dyes and other useful substances. The chemicals plants produce are known as specialized metabolites, and they can change a lot between, and even within, plant species. The variety of specialized metabolites is a result of genetic changes and evolution over millions of years. Evolution is a slow process, yet plants are able to rapidly develop new specialized metabolites to protect them from new threats. Even different populations of the same species produce many distinct metabolites that help them survive in their surroundings. However, the factors that lead plants to produce new metabolites are not well understood, and it is not known how this affects genetic variation. To gain a better understanding of this process, Katz et al. studied 797 European variants of a common weed species called Arabidopsis thaliana, which is widely studied. The investigation found that many factors affect the range of specialized metabolites in each variant. These included local geography and environment, as well as genetics and population history (demography). Katz et al. revealed a pattern of relationships between the variants that could mirror their evolutionary history as the species spread and adapted to new locations. These results highlight the complex network of factors that affect plant evolution. Rapid diversification is key to plant survival in new and changing environments and has resulted in a wide range of specialized metabolites. As such they are of interest both for studying plant evolution and for understanding their ecology. Expanding similar work to more populations and other species will broaden the scope of our ability to understand how plants adapt to their surroundings.

The plant microbiome is a rich biotic environment, comprising numerous taxa. The community structure of these colonizers is constrained by multiple factors, including host-microbe and microbe-microbe interactions, as well as the interplay between the two. While much can be learned from pairwise relationships between individual hosts and microbes, or individual microbes with themselves, the ensemble of interrelations between the host and microbial consortia may lead to different outcomes that are not easily predicted from the individual interactions. Their study can thus provide new insights into the complex relationship between plants and microbes. Of particular importance is how strain-specific such plant-microbe-microbe interactions are, and how they eventually affect plant health. Here, we test strain-level interactions in the phyllosphere between groups of co-existing commensal and pathogenic Pseudomonas among each other and with A. thaliana, by employing synthetic communities of genome-barcoded isolates. We found that commensal Pseudomonas prompted a host response leading to a selective inhibition of a specific pathogenic lineage, resulting in plant protection. The extent of plant protection, however, was dependent on plant genotype, indicating that these effects are host-mediated. There were similar genotype-specific effects on the microbe side, as we could pinpoint an individual Pseudomonas isolate as the predominant cause for this differential interaction. Collectively, our work highlights how within-species genetic differences on both the host and microbe side can have profound effects on host-microbe-microbe dynamics. The paradigm that we have established provides a platform for the study of host-dependent microbe-microbe competition and cooperation in the A. thaliana-Pseudomonas system.

Cation/proton antiporters (CPAs) play a major role in maintaining living cells’ homeostasis. CPAs are commonly divided into two main groups, CPA1 and CPA2, and are further characterized by two main phenotypes: ion selectivity and electrogenicity. However, tracing the evolutionary relationships of these transporters is challenging because of the high diversity within CPAs. Here, we conduct comprehensive evolutionary analysis of 6537 representative CPAs, describing the full complexity of their phylogeny, and revealing a sequence motif that appears to determine central phenotypic characteristics. In contrast to previous suggestions, we show that the CPA1/CPA2 division only partially correlates with electrogenicity. Our analysis further indicates two acidic residues in the binding site that carry the protons in electrogenic CPAs, and a polar residue in the unwound transmembrane helix 4 that determines ion selectivity. A rationally designed triple mutant successfully converted the electrogenic CPA, EcNhaA, to be electroneutral., Cation/proton antiporters (CPAs) play a major role in maintaining living cells’ homeostasis and are divided in two main groups: CPA1 and CPA2. Here authors use a comprehensive evolutionary analysis of 6537 representative CPAs and reveal a sequence motif that determines central phenotypic characteristics.

Since 1992 PredictProtein (https://predictprotein.org) is a one-stop online resource for protein sequence analysis with its main site hosted at the Luxembourg Centre for Systems Biomedicine (LCSB) and queried monthly by over 3,000 users in 2020. PredictProtein was the first Internet server for protein predictions. It pioneered combining evolutionary information and machine learning. Given a protein sequence as input, the server outputs multiple sequence alignments, predictions of protein structure in 1D and 2D (secondary structure, solvent accessibility, transmembrane segments, disordered regions, protein flexibility, and disulfide bridges) and predictions of protein function (functional effects of sequence variation or point mutations, Gene Ontology (GO) terms, subcellular localization, and protein-, RNA-, and DNA binding). PredictProtein’s infrastructure has moved to the LCSB increasing throughput; the use of MMseqs2 sequence search reduced runtime five-fold; user interface elements improved usability, and new prediction methods were added. PredictProtein recently included predictions from deep learning embeddings (GO and secondary structure) and a method for the prediction of proteins and residues binding DNA, RNA, or other proteins. PredictProtein.org aspires to provide reliable predictions to computational and experimental biologists alike. All scripts and methods are freely available for offline execution in high-throughput settings.AvailabilityFreely accessible webserverPredictProtein.org; Source and docker images: github.com/rostlab

The presence of pathogen-specific antibodies in an individual’s blood-sample is used as an indication of previous exposure and infection to that specific pathogen (e.g., virus or bacterium). Measurement of the diagnostic antibodies is routinely achieved using solid phase immuno-assays such as ELISA tests and western blots. Here, we describe a sero-diagnostic approach based on phage-display of epitope arrays we term “Domain-Scan”. We harness Next-generation sequencing (NGS) to measure the serum binding to dozens of epitopes derived from HIV-1 and HCV simultaneously. The distinction of healthy individuals from those infected with either HIV-1 or HCV, is modeled as a machine-learning classification problem, in which each determinant (“domain”) is considered as a feature, and its NGS read-out provides values that correspond to the level of determinant-specific antibodies in the sample. We show that following training of a machine-learning model on labeled examples, we can very accurately classify unlabeled samples and pinpoint the domains that contribute most to the classification. Our experimental/computational Domain-Scan approach is general and can be adapted to other pathogens as long as sufficient training samples are provided.

Antibodies provide a comprehensive record of the encounters with threats and insults to the immune system. The ability to examine the repertoire of antibodies in serum and discover those that best represent "discriminating features" characteristic of various clinical situations, is potentially very useful. Recently, phage display technologies combined with Next-Generation Sequencing (NGS) produced a powerful experimental methodology, coined "Deep-Panning", in which the spectrum of serum antibodies is probed. In order to extract meaningful biological insights from the tens of millions of affinity-selected peptides generated by Deep-Panning, advanced bioinformatics algorithms are a must. In this study, we describe Motifier, a computational pipeline comprised of a set of algorithms that systematically generates discriminatory peptide motifs based on the affinity-selected peptides identified by Deep-Panning. These motifs are shown to effectively characterize antibody binding activities and through the implementation of machine-learning protocols are shown to accurately classify complex antibody mixtures representing various biological conditions.

Hybrid necrosis in plants arises from conflict between divergent alleles of immunity genes contributed by different parents, resulting in autoimmunity. We investigate a severe hybrid necrosis case inArabidopsis thaliana, where the hybrid does not develop past the cotyledon stage and dies three weeks after sowing. Massive transcriptional changes take place in the hybrid, including the upregulation of most NLR disease resistance genes. This is due to an incompatible interaction between the singleton TIR-NLR geneDANGEROUS MIX 10(DM10), which was recently relocated from a larger NLR cluster, and an unlinked locus,DANGEROUS MIX 11 (DM11). There are multipleDM10allelic variants in the globalA. thalianapopulation, several of which have premature stop codons. One of these, which has a truncated LRR domain, corresponds to theDM10risk allele. TheDM10locus and the adjacent genomic region in the risk allele carriers are highly differentiated from those in the non-risk carriers in the globalA. thalianapopulation, suggesting that this allele became geographically widespread only relatively recently. TheDM11risk allele is much rarer and found only in two accessions from southwestern Spain – a region from which theDM10risk haplotype is absent – indicating that the ranges ofDM10andDM11risk alleles may be non-overlapping.

The most common evolutionary events at the molecular level are single-base substitutions, as well as insertions and deletions (indels) of short DNA segments. A large body of research has been devoted to develop probabilistic substitution models and to infer their parameters using likelihood and Bayesian approaches. In contrast, relatively little has been done to model indel dynamics, probably due to the difficulty in writing explicit likelihood functions. Here, we contribute to the effort of modeling indel dynamics by presenting SpartaABC, an approximate Bayesian computation (ABC) approach to infer indel parameters from sequence data (either aligned or unaligned). SpartaABC circumvents the need to use an explicit likelihood function by extracting summary statistics from simulated sequences. First, summary statistics are extracted from the input sequence data. Second, SpartaABC samples indel parameters from a prior distribution and uses them to simulate sequences. Third, it computes summary statistics from the simulated sets of sequences. By computing a distance between the summary statistics extracted from the input and each simulation, SpartaABC can provide an approximation to the posterior distribution of indel parameters as well as point estimates. We study the performance of our methodology and show that it provides accurate estimates of indel parameters in simulations. We next demonstrate the utility of SpartaABC by studying the impact of alignment errors on the inference of positive selection. A C ++ program implementing SpartaABC is freely available in http://spartaabc.tau.ac.il.

Understanding species adaptation at the molecular level has been a central goal of evolutionary biology and genomics research. This important task becomes increasingly relevant with the constant rise in both genotypic and phenotypic data availabilities. The TraitRateProp web server offers a unique perspective into this task by allowing the detection of associations between sequence evolution rate and whole-organism phenotypes. By analyzing sequences and phenotypes of extant species in the context of their phylogeny, it identifies sequence sites in a gene/protein whose evolutionary rate is associated with shifts in the phenotype. To this end, it considers alternative histories of whole-organism phenotypic changes, which result in the extant phenotypic states. Its joint likelihood framework that combines models of sequence and phenotype evolution allows testing whether an association between these processes exists. In addition to predicting sequence sites most likely to be associated with the phenotypic trait, the server can optionally integrate structural 3D information. This integration allows a visual detection of trait-associated sequence sites that are juxtapose in 3D space, thereby suggesting a common functional role. We used TraitRateProp to study the shifts in sequence evolution rate of the RPS8 protein upon transitions into heterotrophy in Orchidaceae. TraitRateProp is available at http://traitrate.tau.ac.il/prop.

Many analyses for the detection of biological phenomena rely on a multiple sequence alignment as input. The results of such analyses are often further studied through parametric bootstrap procedures, using sequence simulators. One of the problems with conducting such simulation studies is that users currently have no means to decide which insertion and deletion (indel) parameters to choose, so that the resulting sequences mimic biological data. Here, we present SpartaABC, a web server that aims to solve this issue. SpartaABC implements an approximate-Bayesian-computation rejection algorithm to infer indel parameters from sequence data. It does so by extracting summary statistics from the input. It then performs numerous sequence simulations under randomly sampled indel parameters. By computing a distance between the summary statistics extracted from the input and each simulation, SpartaABC retains only parameters behind simulations close to the real data. As output, SpartaABC provides point estimates and approximate posterior distributions of the indel parameters. In addition, SpartaABC allows simulating sequences with the inferred indel parameters. To this end, the sequence simulators, Dawg 2.0 and INDELible were integrated. Using SpartaABC we demonstrate the differences in indel dynamics among three protein-coding genes across mammalian orthologs. SpartaABC is freely available for use at http://spartaabc.tau.ac.il/webserver.

The degree of evolutionary conservation of an amino acid in a protein or a nucleic acid in DNA/RNA reflects a balance between its natural tendency to mutate and the overall need to retain the structural integrity and function of the macromolecule. The ConSurf web server (http://consurf.tau.ac.il), established over 15 years ago, analyses the evolutionary pattern of the amino/nucleic acids of the macromolecule to reveal regions that are important for structure and/or function. Starting from a query sequence or structure, the server automatically collects homologues, infers their multiple sequence alignment and reconstructs a phylogenetic tree that reflects their evolutionary relations. These data are then used, within a probabilistic framework, to estimate the evolutionary rates of each sequence position. Here we introduce several new features into ConSurf, including automatic selection of the best evolutionary model used to infer the rates, the ability to homology-model query proteins, prediction of the secondary structure of query RNA molecules from sequence, the ability to view the biological assembly of a query (in addition to the single chain), mapping of the conservation grades onto 2D RNA models and an advanced view of the phylogenetic tree that enables interactively rerunning ConSurf with the taxa of a sub-tree.

SummaryProtein function involves conformational changes, but often, for a given protein, only some of these conformations are known. The missing conformations could be predicted using the wealth of data in the PDB. Most PDB proteins have multiple structures, and proteins sharing one similar conformation often share others as well. The ConTemplate web server (http://bental.tau.ac.il/contemplate) exploits these observations to suggest conformations for a query protein with at least one known conformation (or model thereof). We demonstrate ConTemplate on a ribose-binding protein that undergoes significant conformational changes upon substrate binding. Querying ConTemplate with the ligand-free (or bound) structure of the protein produces the ligand-bound (or free) conformation with a root-mean-square deviation of 1.7 Å (or 2.2 Å); the models are derived from conformations of other sugar-binding proteins, sharing approximately 30% sequence identity with the query. The calculation also suggests intermediate conformations and a pathway between the bound and free conformations.

Classic alignment algorithms utilize scoring functions which maximize similarity or minimize edit distances. These scoring functions account for both insertion-deletion (indel) and substitution events. In contrast, alignments based on stochastic models aim to explicitly describe the evolutionary dynamics of sequences by inferring relevant probabilistic parameters from input sequences. Despite advances in stochastic modeling during the last two decades, scoring-based methods are still dominant, partially due to slow running times of probabilistic approaches. Alignment inference using stochastic models involves estimating the probability of events, such as the insertion or deletion of a specific number of characters. In this work, we present SimBa-SAl, a simulation-based approach to statistical alignment inference, which relies on an explicit continuous time Markov model for both indels and substitutions. SimBa-SAl has several advantages. First, using simulations, it decouples the estimation of event probabilities from the inference stage, which allows the introduction of accelerations to the alignment inference procedure. Second, it is general and can accommodate various stochastic models of indel formation. Finally, it allows computing the maximum-likelihood alignment, the probability of a given pair of sequences integrated over all possible alignments, and sampling alternative alignments according to their probability. We first show that SimBa-SAl allows accurate estimation of parameters of the long-indel model previously developed by Miklos et al. (2004). We next show that SimBa-SAl is more accurate than previously developed pairwise alignment algorithms, when analyzing simulated as well as empirical data sets. Finally, we study the goodness-of-fit of the long-indel and TKF91 models. We show that although the long-indel model fits the data sets better than TKF91, there is still room for improvement concerning the realistic modeling of evolutionary sequence dynamics.

Inference of multiple sequence alignments (MSAs) is a critical part of phylogenetic and comparative genomics studies. However, from the same set of sequences different MSAs are often inferred, depending on the methodologies used and the assumed parameters. Much effort has recently been devoted to improving the ability to identify unreliable alignment regions. Detecting such unreliable regions was previously shown to be important for downstream analyses relying on MSAs, such as the detection of positive selection. Here we developed GUIDANCE2, a new integrative methodology that accounts for: (i) uncertainty in the process of indel formation, (ii) uncertainty in the assumed guide tree and (iii) co-optimal solutions in the pairwise alignments, used as building blocks in progressive alignment algorithms. We compared GUIDANCE2 with seven methodologies to detect unreliable MSA regions using extensive simulations and empirical benchmarks. We show that GUIDANCE2 outperforms all previously developed methodologies. Furthermore, GUIDANCE2 also provides a set of alternative MSAs which can be useful for downstream analyses. The novel algorithm is implemented as a web-server, available at: http: //guidance.tau.ac.il.

It is often assumed that it is unlikely that the same insertion or deletion (indel) event occurred at the same position in two independent evolutionary lineages, and thus, indel-based inference of phylogeny should be less subject to homoplasy compared with standard inference which is based on substitution events. Indeed, indels were successfully used to solve debated evolutionary relationships among various taxonomical groups. However, indels are never directly observed but rather inferred from the alignment and thus indel-based inference may be sensitive to alignment errors. It is hypothesized that phylogenetic reconstruction would be more accurate if it relied only on a subset of reliable indels instead of the entire indel data. Here, we developed a method to quantify the reliability of indel characters by measuring how often they appear in a set of alternative multiple sequence alignments. Our approach is based on the assumption that indels that are consistently present in most alternative alignments are more reliable compared with indels that appear only in a small subset of these alignments. Using simulated and empirical data, we studied the impact of filtering and weighting indels by their reliability scores on the accuracy of indel-based phylogenetic reconstruction. The new method is available as a web-server at http://guidance.tau.ac.il/RELINDEL/.

The classic methodology of inferring a phylogenetic tree from sequence data is composed of two steps. First, a multiple sequence alignment (MSA) is computed. Then, a tree is reconstructed assuming the MSA is correct. Yet, inferred MSAs were shown to be inaccurate and alignment errors reduce tree inference accuracy. It was previously proposed that filtering unreliable alignment regions can increase the accuracy of tree inference. However, it was also demonstrated that the benefit of this filtering is often obscured by the resulting loss of phylogenetic signal. In this work we explore an approach, in which instead of relying on a single MSA, we generate a large set of alternative MSAs and concatenate them into a single SuperMSA. By doing so, we account for phylogenetic signals contained in columns that are not present in the single MSA computed by alignment algorithms. Using simulations, we demonstrate that this approach results, on average, in more accurate trees compared to 1) using an unfiltered MSA and 2) using a single MSA with weights assigned to columns according to their reliability. Next, we explore in which regions of the MSA space our approach is expected to be beneficial. Finally, we provide a simple criterion for deciding whether or not the extra effort of computing a SuperMSA and inferring a tree from it is beneficial. Based on these assessments, we expect our methodology to be useful for many cases in which diverged sequences are analyzed. The option to generate such a SuperMSA is available at http://guidance.tau.ac.il.

Evolutionary analysis of phyletic patterns (phylogenetic profiles) is widely used in biology, representing presence or absence of characters such as genes, restriction sites, introns, indels and methylation sites. The phyletic pattern observed in extant genomes is the result of ancestral gain and loss events along the phylogenetic tree. Here we present CoPAP (coevolution of presence–absence patterns), a user-friendly web server, which performs accurate inference of coevolving characters as manifested by co-occurring gains and losses. CoPAP uses state-of-the-art probabilistic methodologies to infer coevolution and allows for advanced network analysis and visualization. We developed a platform for comparing different algorithms that detect coevolution, which includes simulated data with pairs of coevolving sites and independent sites. Using these simulated data we demonstrate that CoPAP performance is higher than alternative methods. We exemplify CoPAP utility by analyzing coevolution among thousands of bacterial genes across 681 genomes. Clusters of coevolving genes that were detected using our method largely coincide with known biosynthesis pathways and cellular modules, thus exhibiting the capability of CoPAP to infer biologically meaningful interactions. CoPAP is freely available for use at http://copap.tau.ac.il/.

Many mutations disappear from the population because they impair protein function and/or stability. Thus, amino acid positions that are essential for proper function evolve more slowly than others, or in other words, the slow evolutionary rate of a position reflects its importance. Con- Surf (http://consurf.tau.ac.il), reviewed in this manuscript, exploits this to reveal key amino acid positions that are im- portant for maintaining the native conformation(s) of the protein and its function, be it binding, catalysis, transport, etc. Given the sequence or 3D structure of the query protein as input, a search for similar sequences is conducted and the sequences are aligned. The multiple sequence alignment is subsequently used to calculate the evolutionary rates of each amino acid site, using Bayesian or maximum-likelihood algorithms. Both algorithms take into account the evolution- ary relationships between the sequences, reflected in phylo- genetic trees, to alleviate problems due to uneven (biased) sampling in sequence space. This is particularly important when the number of sequences is low. The ConSurf-DB, a new release of which is presented here, provides precalcu- lated ConSurf conservation analysis of nearly all available structures in the Protein DataBank (PDB). The usefulness of ConSurf for the study of individual proteins and mutations, as well as a range of large-scale, genome-wide applications, is reviewed.

Motivation: Accurate prediction of protein stability is important for understanding the molecular underpinnings of diseases and for the design of new proteins. We introduce a novel approach for the prediction of changes in protein stability that arise from a single-site amino acid substitution; the approach uses available data on mutations occurring in the same position and in other positions. Our algorithm, named Pro-Maya (Protein Mutant stAbilitY Analyzer), combines a collaborative filtering baseline model, Random Forests regression and a diverse set of features. Pro-Maya predicts the stability free energy difference of mutant versus wild type, denoted as ΔΔG. Results: We evaluated our algorithm extensively using cross-validation on two previously utilized datasets of single amino acid mutations and a (third) validation set. The results indicate that using known ΔΔG values of mutations at the query position improves the accuracy of ΔΔG predictions for other mutations in that position. The accuracy of our predictions in such cases significantly surpasses that of similar methods, achieving, e.g. a Pearson's correlation coefficient of 0.79 and a root mean square error of 0.96 on the validation set. Because Pro-Maya uses a diverse set of features, including predictions using two other methods, it also performs slightly better than other methods in the absence of additional experimental data on the query positions. Availability: Pro-Maya is freely available via web server at http://bental.tau.ac.il/ProMaya. Contact: nirb@tauex.tau.ac.il; wolf@cs.tau.ac.il Supplementary Information: Supplementary data are available at Bioinformatics online.

Evaluating the accuracy of multiple sequence alignment (MSA) is critical for virtually every comparative sequence analysis that uses an MSA as input. Here we present the GUIDANCE web-server, a user-friendly, open access tool for the identification of unreliable alignment regions. The web-server accepts as input a set of unaligned sequences. The server aligns the sequences and provides a simple graphic visualization of the confidence score of each column, residue and sequence of an alignment, using a color-coding scheme. The method is generic and the user is allowed to choose the alignment algorithm (ClustalW, MAFFT and PRANK are supported) as well as any type of molecular sequences (nucleotide, protein or codon sequences). The server implements two different algorithms for evaluating confidence scores: (i) the heads-or-tails (HoT) method, which measures alignment uncertainty due to co-optimal solutions; (ii) the GUIDANCE method, which measures the robustness of the alignment to guide-tree uncertainty. The server projects the confidence scores onto the MSA and points to columns and sequences that are unreliably aligned. These can be automatically removed in preparation for downstream analyses. GUIDANCE is freely available for use at http://guidance.tau.ac.il.